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train-model
/ 0.1.0
Train Random Forest on Zarr Data with Cross-Validation
A solution that trains a Random Forest model using data from a Zarr zip store, filters runs with only one label, and performs 10-fold cross-validation.
Tags
imaging
cryoet
Python
napari
Solution written by
Kyle Harrington
License of solution
MIT
Source Code
View on GitHub
Arguments
--input_zarr_path
Path to the input Zarr zip store containing the features and labels. (default value: PARAMETER_VALUE)
--output_model_path
Path for the output joblib file containing the trained Random Forest model. (default value: PARAMETER_VALUE)
--n_estimators
Number of trees in the Random Forest. (default value: PARAMETER_VALUE)
Usage instructions
Please follow
this link
for details on how to install and run this solution.